National AI plans should encourage international collaboration: expert

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Tom Mitchell, professor at The School of Computer Science at Carnegie Mellon University, speaking at WAIC on August 29, 2019. (Image credit: WAIC)

National artificial intelligence (AI) plans, including those drafted by China, should promote international collaboration, not just permit it, according to Tom Mitchell, former dean of Carnegie Mellon University’s computer science school.

Why it matters: Mitchell, known as the father of machine learning, was speaking at the opening ceremony of the World Artificial Intelligence Conference (WAIC) in Shanghai on Thursday.

  • Shanghai is attempting to reimagine itself as an international AI capital, and for the city, the WAIC event forms a major part of this push.
  • China hopes to become a world leader in AI in the next ten years and has laid out plans to meet this goal.

“What I think these national strategies need is a distinction that says for win-win applications the rational strategy for every country is not just to allow collaboration but actually to promote it. And to find ways to, for example, share medical data internationally, and share algorithms and the hard engineering work.”

—Tom Mitchell, at WAIC on Thursday

Details: Mitchell said that AI applications in healthcare, education, and smart cities could benefit from researchers in different countries working together.

  • Collaboration on AI could improve the general quality of life, as working together could accelerate development, Mitchell said.
  • Mitchell hopes Chinese policymakers will encourage collaboration into their future AI plans.

Context: China has laid out goals that policymakers hope will make the country an AI trailblazer by 2030.

  • The country is currently working to catch up with the US in its AI capabilities. According to numerous reports, China falls behind in terms of talent and hardware, but leads in deployment and data.
  • China has strict rules governing the transfer of personal data abroad, which becomes ever more sensitive when the data is medical in nature.